Geri Skenderi
Papers
2
Total Citations
43
H-Index
2
About
Geri Skenderi is a leading researcher in human-robot collaboration, with a primary focus on advancing pose forecasting for industrial applications. Their most impactful work, published in 2022, introduces the Separable-Sparse Graph Convolutional Network (SeS-GCN), a novel architecture that for the first time bottlenecks the interaction of spatial, temporal, and channel-wise dimensions in graph convolutional networks. This breakthrough enables more accurate and efficient prediction of human poses, directly addressing critical safety and coordination challenges in collaborative robotics. With their top-cited paper accumulating 41 citations, Skenderi’s contributions are gaining traction in the robotics and computer vision communities. By pushing back the frontiers of industrial human-robot interaction, their work lays the groundwork for smarter, more responsive automation systems where humans and machines can work side by side seamlessly. Skenderi’s research is essential reading for anyone interested in the intersection of deep learning, graph neural networks, and real-world robotics.
Research Focus
Key Achievements
Top Papers
- 1Pose Forecasting in Industrial Human-Robot Collaboration41 citations · 2022
- 2Pose Forecasting in Industrial Human-Robot Collaboration2 citations · 2022